Abstract
In this chapter we present the recent results on constraint acquisition obtained by the Coconut team and their collaborators. In a first part we show how to learn constraint networks by asking the user partial queries. That is, we ask the user to classify assignments to subsets of the variables as positive or negative. We provide an algorithm, called QuAcq, that, given a negative example, finds a constraint of the target network in a number of queries logarithmic in the size of the example. In a second part, we show that using some background knowledge may improve the acquisition process a lot. We introduce the concept of generalization query based on an aggregation of variables into types. We propose a generalization algorithm together with several strategies that we incorporate in QuAcq. Finally we evaluate our algorithms on some benchmarks.
Sections 3 and 4 of this paper describe material published in [9], Sect. 5 describes material published in [8], and Sect. 6 describes results coming from both of these two papers.
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Notes
- 1.
This operation could proactively be done in QuAcq, just after line 11, but we preferred the lazy mode as this is a computationally expensive operation.
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Bessiere, C. et al. (2016). New Approaches to Constraint Acquisition. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O'Sullivan, B., Pedreschi, D. (eds) Data Mining and Constraint Programming. Lecture Notes in Computer Science(), vol 10101. Springer, Cham. https://doi.org/10.1007/978-3-319-50137-6_3
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